| mdlpreds | mean(r2) | median(r2) | sd(r2) |
|---|---|---|---|
| phv | 0.52 | 0.56 | 0.09 |
| phvfsca | 0.76 | 0.77 | 0.06 |
| phvaso | 0.67 | 0.71 | 0.17 |
| phvasofsca | 0.79 | 0.79 | 0.09 |
| mdlpreds | mean(pctmae) | median(pctmae) | sd(pctmae) |
|---|---|---|---|
| phv | 59.38 | 54.39 | 21.96 |
| phvfsca | 37.14 | 37.61 | 11.93 |
| phvaso | 53.98 | 45.90 | 23.32 |
| phvasofsca | 36.21 | 35.70 | 12.32 |
comparison of maps for each date but leaving out maps from the same year in the comparison
more dates are statistically significant with mae so we’ll stick with that.
this doesn’t show much. errors go up end of season while mean swe goes down….
again not a very useful plot. errors go up as fsca mae goes down (due to smaller differences when there is less snow cover)
see “figs/so_many_fscaplots.pdf” for all plots
| simdte | phv | phvfsca | phvaso | phvasofsca | sep | simdte.1 | phv.1 | phvfsca.1 | phvaso.1 | phvasofsca.1 |
| 2013-04-03 | -0.24 | -0.07 | -0.26 | - | | | 2013-04-03 | 87.25 | - | 113.44 | - |
| 2013-04-29 | -0.24 | - | -0.32 | -0.17 | | | 2013-04-29 | 73.76 | - | 100.15 | - |
| 2013-05-03 | -0.19 | - | - | - | | | 2013-05-03 | 60.13 | - | 67.53 | - |
| 2013-05-25 | - | -0.09 | -0.18 | -0.18 | | | 2013-05-25 | 163.89 | - | 213.94 | - |
| 2013-06-01 | -0.09 | -0.15 | -0.16 | -0.24 | | | 2013-06-01 | 285.64 | - | 360.21 | - |
| 2013-06-08 | -0.13 | -0.25 | -0.19 | -0.37 | | | 2013-06-08 | 618.97 | 157.38 | 762.56 | - |
| 2014-03-23 | -0.20 | - | -0.16 | - | | | 2014-03-23 | 74.09 | -52.27 | 97.27 | -51.56 |
| 2014-04-07 | -0.20 | - | -0.17 | - | | | 2014-04-07 | 72.32 | -54.53 | 92.60 | -52.38 |
| 2014-04-13 | -0.15 | - | -0.12 | - | | | 2014-04-13 | 56.55 | -65.95 | 87.28 | -54.56 |
| 2014-04-20 | - | - | - | - | | | 2014-04-20 | - | - | 93.80 | - |
| 2014-04-28 | - | - | - | -0.20 | | | 2014-04-28 | - | - | 81.06 | - |
| 2014-05-02 | - | - | - | - | | | 2014-05-02 | - | - | - | - |
| 2014-05-11 | - | - | - | - | | | 2014-05-11 | - | - | - | - |
| 2014-05-17 | -0.28 | -0.20 | -0.41 | -0.44 | | | 2014-05-17 | - | - | - | - |
| 2014-05-27 | -0.29 | -0.28 | -0.42 | -0.49 | | | 2014-05-27 | 499.44 | - | 670.84 | - |
| 2014-05-31 | -0.27 | -0.30 | -0.37 | -0.54 | | | 2014-05-31 | 730.34 | - | 935.01 | - |
| 2015-02-17 | -0.54 | -0.33 | -0.50 | - | | | 2015-02-17 | - | - | - | - |
| 2015-03-05 | -0.29 | -0.11 | -0.32 | -0.17 | | | 2015-03-05 | - | - | - | - |
| 2015-03-25 | - | - | - | 0.08 | | | 2015-03-25 | - | - | 292.38 | - |
| 2015-04-09 | -0.16 | -0.19 | -0.22 | -0.24 | | | 2015-04-09 | - | - | - | - |
| 2015-04-15 | - | - | - | - | | | 2015-04-15 | 254.24 | - | 358.00 | - |
| 2015-04-27 | -0.28 | -0.10 | -0.21 | - | | | 2015-04-27 | - | - | - | - |
| 2015-05-01 | - | - | -0.07 | -0.12 | | | 2015-05-01 | 546.20 | - | 735.10 | - |
| 2015-06-08 | -0.14 | -0.23 | -0.19 | -0.57 | | | 2015-06-08 | 1298.93 | 377.43 | 1466.82 | 543.49 |
| 2016-05-27 | -0.12 | -0.12 | - | -0.15 | | | 2016-05-27 | 73.24 | - | 86.43 | - |
| 2016-06-07 | -0.19 | - | - | - | | | 2016-06-07 | 54.77 | - | 67.80 | - |
| 2016-06-13 | -0.16 | - | -0.18 | -0.16 | | | 2016-06-13 | 109.99 | - | 158.43 | - |
| 2016-06-20 | -0.11 | -0.15 | -0.17 | -0.30 | | | 2016-06-20 | 183.25 | 28.92 | 261.79 | 19.85 |
| mdlpreds | var | mean(val) | sd(val) |
|---|---|---|---|
| phv | diff | -0.03 | 0.02 |
| phvaso | diff | -0.04 | 0.04 |
| phvasofsca | diff | -0.04 | 0.06 |
| phvfsca | diff | -0.02 | 0.02 |
| errorvar | mdlpreds | var | mean(val, na.rm = T) | sd(val, na.rm = T) |
|---|---|---|---|---|
| r2 | phv | bestpred | 0.52 | 0.09 |
| r2 | phv | lowestfscamae | 0.50 | 0.10 |
| r2 | phvaso | bestpred | 0.67 | 0.17 |
| r2 | phvaso | lowestfscamae | 0.63 | 0.18 |
| r2 | phvasofsca | bestpred | 0.79 | 0.09 |
| r2 | phvasofsca | lowestfscamae | 0.75 | 0.10 |
| r2 | phvfsca | bestpred | 0.76 | 0.06 |
| r2 | phvfsca | lowestfscamae | 0.74 | 0.06 |
| mdlpreds | var | mean(val) | sd(val) |
|---|---|---|---|
| phv | diff | 22.70 | 38.00 |
| phvaso | diff | 23.83 | 34.42 |
| phvasofsca | diff | 30.42 | 29.96 |
| phvfsca | diff | 29.93 | 40.43 |
| errorvar | mdlpreds | var | mean(val, na.rm = T) | sd(val, na.rm = T) |
|---|---|---|---|---|
| pctmae | phv | bestpred | 59.38 | 21.96 |
| pctmae | phv | lowestfscamae | 82.08 | 39.06 |
| pctmae | phvaso | bestpred | 53.98 | 23.32 |
| pctmae | phvaso | lowestfscamae | 77.81 | 37.47 |
| pctmae | phvasofsca | bestpred | 36.21 | 12.32 |
| pctmae | phvasofsca | lowestfscamae | 66.63 | 27.49 |
| pctmae | phvfsca | bestpred | 37.14 | 11.93 |
| pctmae | phvfsca | lowestfscamae | 67.06 | 39.15 |
More yellow means prediction was better. x and y axis represent the differences between simulation date and model date expressed as mean squared error or mean absolute error.
Again not that useful to look at because prediction error is lowest when fscamae is highest (due to more snow cover earlier in season). Not immediately intuitive until you look at fscamae vs date.